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Frontiers of Information Technology (FIT)

作者： Ayesha Shoukat Shahzad Akbar Syed Al E Hassan Amjad Rehman Noor Ayesha Riphah International University Faisalabad Campus Pakistan Artificial Intelligence & Data Analytics Lab (AIDA) Prince Sultan University Riyadh Saudi Arabia School of Clinical Medicine Zhengzhou University Zhengzhou Henan RP China

ISBN: (纸本)9781665408318

One of the most dangerous diseases that affect the vision of the human eye is glaucoma. It is a retinal illness that damages the ONH (Optic Nerve Head) and results in everlasting blindness if it is diagnosed at an advanced stage. Permanent blindness can only be avoided through early-stage detection and treatment of glaucoma. This paper presents a model based on the architectures of convolutional neural networks that can detect glaucoma at an early stage using the fundus images. Three datasets, G1020, RIM-ONE and REFUGE, are used in the proposed model. The images are enhanced through the CLAHE (Contrast Limited Adaptive Histogram Equalization) approach and a median filter is applied to reduce the noise during preprocessing step. The variety of images is created to efficiently train the proposed model by using the data augmentation approach. Three pre-trained CNN (Convolutional Neural Network) architectures such as VGGl9, ResNet50 and EfficientNetB7 are used for the classification, and their performance is compared through different performance measures. The best classification results are achieved through the EfficieientNetB7 architecture with accuracy of 99.2%, sensitivity of 98% and specificity of 97% on G1020 dataset. The robust results obtained by the proposed method have smoothened the way towards the fast and reliable glaucoma detection system It will help the ophthalmologists and the clinicians to screen the masses and make reliable decisions about the glaucoma diagnosis.

关键词： Optical filters Deep learning Adaptation models Sensitivity Blindness Retina Reliability

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data Augmentation and Optimizer Tuning for Polyp Segmentation

Data Augmentation and Optimizer Tuning for Polyp Segmentatio...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Zeeshan Haider Ahmad Taher Azar Tanzila Saba Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Artificial Intelligence & Data Analytics (AIDA) Lab Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Automatic segmentation of polyps is a very challenging problem in the scope of medical imaging. This challenge is often faced due to a lack of quality datasets. In this research, we explore the effect on the accuracy of colorectal cancer polyp segmentation due to augmentation and various optimizers while training the segmentation model. The effect of augmentation and optimizers on the polyp segmentation is studied separately. The augmentation effect is observed by changing the percentage of augmentation in each experiment whereas the optimizer effect is studied by changing the optimizer for each experiment. The experiments are performed with 8 optimizers and 10 different augmentation strategies.

关键词： Training Image segmentation Automation Accuracy Training data data augmentation Tuning

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Finding stable matchings in PhD markets with consistent preferences and cooperative partners

arXiv

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arXiv 2021年

作者： Mordig, Maximilian Vecchia, Riccardo Della Cesa-Bianchi, Nicolò Schölkopf, Bernhard Mpi for Intelligent Systems Eth Zürich Artificial Intelligence Lab Institute for Data Science and Analytics Bocconi University Milano Italy Dipartimento di Informatica Dsrc Università Degli Studi di Milano Milano Italy

We introduce a new algorithm for finding stable matchings in multi-sided matching markets. Our setting is motivated by a PhD market of students, advisors, and co-advisors, and can be generalized to supply chain networks viewed as n-sided markets. In the three-sided PhD market, students primarily care about advisors and then about co-advisors (consistent preferences), while advisors and co-advisors have preferences over students only (hence they are cooperative). A student must be matched to one advisor and one co-advisor, or not at all. In contrast to previous work, advisor-student and student-co-advisor pairs may not be mutually acceptable (e.g., a student may not want to work with an advisor or co-advisor and vice versa). We show that three-sided stable matchings always exist, and present an algorithm that, in time quadratic in the market size (up to log factors), finds a three-sided stable matching using any two-sided stable matching algorithm as matching engine. We illustrate the challenges that arise when not all advisor-co-advisor pairs are compatible. We then generalize our algorithm to n-sided markets with quotas and show how they can model supply chain networks. Finally, we show how our algorithm outperforms the baseline given by [Danilov, 2003] in terms of both producing a stable matching and a larger number of matches on a synthetic dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Students

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AI-Enhanced Mammographic Screening: Optimizing Breast Lesion Detection and Classification with the Robust ECA-Net152 Model

AI-Enhanced Mammographic Screening: Optimizing Breast Lesion...

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Computer, Big data and artificial intelligence (ICCBD+AI), International Conference on

作者： Tariq Mahmood Tanzila Saba Amjad Rehman Xiang Zhang Muhammad Yaqub Artificial Intelligence and Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Kingdom of Saudi Arabia Chinese Academy of Sciences Institute of Biophysics Beijing China School of Biomedical Sciences Hunan University Changsha China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Breast cancer poses a significant threat to women’s health, and early screening and diagnosis are crucial for improving treatment outcomes and reducing mortality. Computer-aided diagnosis (CAD) systems have been developed to help physicians classify benign and malignant lesions in mammograms. The paper introduces a new pre-processing technique for mammographic datasets, addressing issues like tissue overlap and image noise. The technique includes denoising, segmentation, contrast adjustment, and data augmentation algorithms, enhancing CNN input and generalization. The ECA-Net152 model, which combines channel attention and focal loss function, is introduced to classify breast lesions. The method achieves an AUC of 0.960 and performs better through experimental comparisons and GradCAM visualization. This study contributes to AI-assisted diagnosis and early screening of breast cancer.

关键词： Solid modeling Transfer learning Noise reduction Noise data visualization Mortality Medical services Breast cancer Mammography Lesions

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Deep Learning Approach to Diagnose Alzheimer’s Disease through Magnetic Resonance Images

Deep Learning Approach to Diagnose Alzheimer’s Disease thro...

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International Conference on Innovative Computing (ICIC)

作者： Muhammad Faraz Ahmad Shahzad Akbar Syed Al E Hassan Amjad Rehman Noor Ayesha Ripah College of Computing Riphah International University Faisalabad Pakistan Artificial Intelligence & Data Analytics Lab (AIDA) Prince Sultan University Riyadh Saudi Arabia School of Clinical Medicine Zhengzhou University Zhengzhou Henan RP China

ISBN: (纸本)9781665400923

Alzheimer’s disease (AD) is the disease that is neurodegenerative which causes 70 to 80% of cases of dementia worldwide. Even though over the past few years’ research on Alzheimer’s disease has risen, Although, the brain function and structure complexity enables diagnosis still a difficult task. In recent years, Machine Learning approaches have played an essential role in understanding the deep analysis of diseases detection with the help of drug delivery and image processing units in biomedical science. These techniques have enhanced the efficiency and ability to understand complex medical situations. This paper investigates a Convolutional Neural Networks (CNN) based Algorithm of Deep learning and Computer-Aided Diagnosis (CAD) for the Alzheimer’s disease (AD) early diagnosis by differentiating the affected brain and normal brain. Since detection of AD has been a difficult task. However, using CNN-based CAD, we successfully classify the MRI data of AD subjects with an accuracy rate of 97%. This research suggests that Deep Learning algorithms are the most successful methods for classifying clinical MRI subjects by the scale and shift homogeneous distill features extracted by a Convolutional Neural Network. In addition, this approach will help doctors and clinicians to understand and predict more complex systems in the biomedical field.

关键词： Deep learning Magnetic resonance imaging Information filters Feature extraction Classification algorithms Convolutional neural networks Gabor filters

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and artificial Bee Colony 22

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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Proceedings of the 6th International Conference on Advances in artificial intelligence

作者： Mohamad Faiz Dzulkalnine Mohd Saberi Mohamad Yee Wen Choon Muhammad Akmal Remli Hany Alashwal Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Malaysia and Department of Data Science Universiti Malaysia Kelantan Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE and Big Data Analytics Center United Arab Emirates University UAE

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms’ cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy.

关键词： Gene Knockouts Particle Swarm Optimization artificial Bee Colony Ethanol Flux Balance Analysis

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Shaping the learning landscape in neural networks around wide flat minima

arXiv

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arXiv 2019年

作者： Baldassi, Carlo Pittorino, Fabrizio Zecchina, Riccardo Artificial Intelligence Lab Institute for Data Science and Analytics Bocconi University Milano Italy Istituto Nazionale di Fisica Nucleare Sezione di Torino Italy Politecnico di Torino Italy International Centre for Theoretical Physics Trieste Italy

Learning in Deep Neural Networks (DNN) takes place by minimizing a non-convex high-dimensional loss function, typically by a stochastic gradient descent (SGD) strategy. The learning process is observed to be able to find good minimizers without getting stuck in local critical points, and that such minimizers are often satisfactory at avoiding overfitting. How these two features can be kept under control in nonlinear devices composed of millions of tunable connections is a profound and far reaching open question. In this paper we study basic non-convex neural network models which learn random patterns, and derive a number of basic geometrical and algorithmic features which suggest some answers. We first show that the error loss function presents few extremely wide flat minima (WFM) which coexist with narrower minima and critical points. We then show that the minimizers of the cross-entropy loss function overlap with the WFM of the error loss. We also show examples of learning devices for which WFM do not exist. From the algorithmic perspective we derive entropy driven greedy and message passing algorithms which focus their search on wide flat regions of minimizers. In the case of SGD and cross-entropy loss, we show that a slow reduction of the norm of the weights along the learning process also leads to WFM. We corroborate the results by a numerical study of the correlations between the volumes of the minimizers, their Hessian and their generalization performance on real data. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Covid-19 Detection by using Deep learning-based Custom Convolution Neural Network (CNN)

Covid-19 Detection by using Deep learning-based Custom Convo...

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International Conference on Innovative Computing (ICIC)

作者： Mazhar Javed Awan Muhammad Wasif Imtiaz Muhammad Usama Amjad Rehman Noor Ayesha Hafiz Muhammad Faisal Shehzad University of Management and Technology Lahore Pakistan Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University Riyadh Saudi Arabia School of Clinical Medicine Zhengzhou University Zhengzhou Henan RP China University of Sargodha Pakistan

ISBN: (纸本)9781665400923

The early diagnosis and treatment of COVID-19 has been a challenge all over the world. It is challenging to manufacture many testing kits and even then, their accuracy rate is very low. Studies carried out recently show that chest x-ray images are of great help in the diagnosis of COVID-19. In this study, we have developed a COVID-19 detection model that by observing the chest x-ray images of the patient, detects that either the patient is affected by COVID-19 or not. The model is developed using a custom Convolutional Neural Network (CNN) that differentiates between COVID-19 and healthy x-ray images so that the patient can be diagnosed and quarantined on time to prevent the spread of the pandemic. We used two different datasets which are publicly available for the training and validation of this model. Upon completion, the proposed model yields an accuracy of almost 98%. Upon further training, our model will be able to be used as a COVID-19 detection tool in hospitals worldwide and will play a vital role in early detection and timely containment of the pandemic.

关键词： COVID-19 Training Pandemics Hospitals Neural networks Convolutional neural networks X-ray imaging

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Compiler-Assisted Kernel Selection for FPGA-based Near-Memory Computing Platforms

Compiler-Assisted Kernel Selection for FPGA-based Near-Memor...

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Veronia Iskandar Mohamed A. Abd El Ghany Diana Goehringer Chair of Adaptive Dynamic Systems Technische Universität Dresden Germany Electronics Department IES Lab German University in Cairo Egypt/ TU Darmstadt Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany

The speed of modern computing systems has improved significantly, thanks to advances in CMOS technology. However, the memory bandwidth of DRAM has not kept pace with these improvements in terms of latency and energy consumption, which is known as the memory wall [1]. FPGAs with high-bandwidth memory (HBM) provide significantly improved performance on memory-intensive tasks, such as graph processing and machine learning. By leveraging 3D-stacked DRAM memory on FPGAs, it is possible to realize the Near-Memory Computing (NMC) paradigm, which involves offloading some kernels to be processed close to the memory. While there have been many studies on NMC accelerators, there is no established method for determining which application kernels are suitable for execution near the HBM. To fully realize the potential of FPGA-HBM architectures, it is important to identify offloading candidates without relying on programmers' knowledge. However, this is a non-trivial task due to the complexity of modern applications. To address this issue, we propose a compiler-assisted tool-flow for the automatic selection of kernels to be offloaded.

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Performance Estimation and Prototyping of Reconfigurable Near-Memory Computing Systems

Performance Estimation and Prototyping of Reconfigurable Nea...

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International Conference on Field Programmable Logic and Applications

作者： Veronia Iskandar Mohamed A. Abd El Ghany Diana Goehringer Chair of Adaptive Dynamic Systems Technische Universität Dresden Germany Electronics Department IES Lab German University in Cairo Egypt TU Darmstadt Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany

The concept of near-memory computing (NMC) has emerged as a promising solution to address the memory wall challenges faced by future computing architectures. By utilizing modern systems that integrate 3D-stacked DRAM memory, the NMC paradigm minimizes unnecessary data movement between the memory subsystem and the CPU. FPGA vendors have incorporated 3D-stacked memories into their products to meet the increasing bandwidth requirements of memory-intensive applications, enabling FPGAs to compete with GPU solutions in terms of speed and energy efficiency. Recent NMC proposals focus on different data processing workloads, including graph processing and machine learning. This work addresses the research questions of how to leverage the full bandwidth of 3D-stacked high-bandwidth memory and how to facilitate the adoption of the near-memory computing paradigm.

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